Pu-Ming Zhang

Spike sorting based on automatic template reconstruction with a partial solution to the overlapping problem

A new method for spike sorting is proposed which partly solves the overlapping problem. Principal component analysis and subtractive clustering techniques are used to estimate the number of neurons contributing to multi-unit recording. Spike templates (i.e. waveforms) are reconstructed according to the clustering results. A template-matching procedure is then performed. Firstly all temporally displaced templates are compared with the spike event to find the best-fitting template that yields the minimum residue variance. If the residue passes the chi(2)-test, the matching procedure stops and the spike event is classified as the best-fitting template. Otherwise the spike event may be an overlapping waveform. The procedure is then repeated with all possible combinations of two templates, three templates, etc. Once one combination is found, which yields the minimum residue variance among the combinations of the same number of component templates and makes the residue pass the chi(2)-test, the matching procedure stops. It is unnecessary to check the remaining combinations of more templates. Consequently, the computational effort is reduced and the over-fitting problem can be partly avoided. A simulated spike train was used to assess the performance of the proposed method, which was also applied to a real recording of chicken retina ganglion cells.

A robust method for spike sorting with automatic overlap decomposition

Spike sorting is the mandatory first step in analyzing multiunit recording signals for studying information processing mechanisms within the nervous system. Extracellular recordings usually contain overlapped spikes produced by a number of neurons adjacent to the electrode, together with unknown background noise, which in turn induce some difficulties in neural signal identification. In this paper, we propose a robust method to deal with these problems, which employs an automatic overlap decomposition technique based on the relaxation algorithm that requires simple fast Fourier transforms. The performance of the presented system was tested at various signal-to-noise ratio levels based on synthetic data that were generated from real recordings.

Spatiotemporal dynamics of high-K + -induced epileptiform discharges in hippocampal slice and the effects of valproate

The epileptic seizure is a dynamic process involving a rapid transition from normal activity to a state of hypersynchronous neuronal discharges. Here we investigated the network properties of epileptiform discharges in hippocampal slices in the presence of high K+ concentration (8.5 mmol/L) in the bath, and the effects of the anti-epileptic drug valproate (VPA) on epileptiform discharges, using a microelectrode array. We demonstrated that epileptiform discharges were predominantly initiated from the stratum pyramidale layer of CA3a-b and propagated bi-directionally to CA1 and CA3c. Disconnection of CA3 from CA1 abolished the discharges in CA1 without disrupting the initiation of discharges in CA3. Further pharmacological experiments showed that VPA at a clinically relevant concentration (100 μmol/L) suppressed the propagation speed but not the rate or duration of high-K+-induced discharges. Our findings suggest that pacemakers exist in the CA3a-b region for the generation of epileptiform discharges in the hippocampus. VPA reduces the conduction of such discharges in the network by reducing the propagation speed.

Involvement of Thalamus in Initiation of Epileptic Seizures Induced by Pilocarpine in Mice

Studies have suggested that thalamus is involved in temporal lobe epilepsy, but the role of thalamus is still unclear. We obtained local filed potentials (LFPs) and single-unit activities from CA1 of hippocampus and parafascicular nucleus of thalamus during the development of epileptic seizures induced by pilocarpine in mice. Two measures, redundancy and directionality index, were used to analyze the electrophysiological characters of neuronal activities and the information flow between thalamus and hippocampus. We found that LFPs became more regular during the seizure in both hippocampus and thalamus, and in some cases LFPs showed a transient disorder at seizure onset. The variation tendency of the peak values of cross-correlation function between neurons matched the variation tendency of the redundancy of LFPs. The information tended to flow from thalamus to hippocampus during seizure initiation period no matter what the information flow direction was before the seizure. In some cases the information flow was symmetrically bidirectional, but none was found in which the information flowed from hippocampus to thalamus during the seizure initiation period. In addition, inactivation of thalamus by tetrodotoxin (TTX) resulted in a suppression of seizures. These results suggest that thalamus may play an important role in the initiation of epileptic seizures.

Neural coding properties based on spike timing and pattern correlation of retinal ganglion cells

Correlation between spike trains or neurons sometimes indicates certain neural coding rules in the visual system. In this paper, the relationship between spike timing correlation and pattern correlation is discussed, and their ability to represent stimulus features is compared to examine their coding strategies not only in individual neurons but also in population. Two kinds of stimuli, natural movies and checkerboard, are used to arouse firing activities in chicken retinal ganglion cells. The spike timing correlation and pattern correlation are calculated by cross-correlation function and Lempel–Ziv distance respectively. According to the correlation values, it is demonstrated that spike trains with similar spike patterns are not necessarily concerted in firing time. Moreover, spike pattern correlation values between individual neurons’ responses reflect the difference of natural movies and checkerboard; neurons cooperate with each other with higher pattern correlation values which represent spatiotemporal correlations during response to natural movies. Spike timing does not reflect stimulus features as obvious as spike patterns, caused by their particular coding properties or physiological foundation. As a result, separating the pattern correlation out of traditional timing correlation concept uncover additional insight in neural coding.

Effective Connectivity of Hippocampal Neural Network and Its Alteration in Mg2+-Free Epilepsy Model

Understanding the connectivity of the brain neural network and its evolution in epileptiform discharges is meaningful in the epilepsy researches and treatments. In the present study, epileptiform discharges were induced in rat hippocampal slices perfused with Mg2+-free artificial cerebrospinal fluid. The effective connectivity of the hippocampal neural network was studied by comparing the normal and epileptiform discharges recorded by a microelectrode array. The neural network connectivity was constructed by using partial directed coherence and analyzed by graph theory. The transition of the hippocampal network topology from control to epileptiform discharges was demonstrated. Firstly, differences existed in both the averaged in- and out-degree between nodes in the pyramidal cell layer and the granule cell layer, which indicated an information flow from the pyramidal cell layer to the granule cell layer during epileptiform discharges, whereas no consistent information flow was observed in control. Secondly, the neural network showed different small-worldness in the early, middle and late stages of the epileptiform discharges, whereas the control network did not show the small-world property. Thirdly, the network connectivity began to change earlier than the appearance of epileptiform discharges and lasted several seconds after the epileptiform discharges disappeared. These results revealed the important network bases underlying the transition from normal to epileptiform discharges in hippocampal slices. Additionally, this work indicated that the network analysis might provide a useful tool to evaluate the neural network and help to improve the prediction of seizures.

A simulation study on the encoding mechanism of retinal ganglion cell

Understanding how the retina encodes visual information is a key issue for the development of a retinal prosthesis. To study this issue, the neural retina is modeled as a retina module (RM) consisted of an ensemble of spatial-temporal (ST) filters and each ST filter simulates the input-output property of an individual ganglion cell (GC). Two receptive field (RF) models of retinal GC, the difference of Gaussians (DOG) model and the disinhibition (DIS) model, are employed to implement these ST filters respectively. RM performs the encoding operation from an input optical pattern to a group of parallel action potential (AP) trains. To assess the encoding efficiency of RF models, a central visual system module (VM) consisted of a group of artificial neural networks is employed to perform the decoding operation from AP trains to an output perceptual pattern. A matching error is defined as an index to quantify the similarity between the input optical pattern and the output perceptual pattern generated by VM. The simulation results suggest that the matching error declines dramatically when the DOG model is replaced by the DIS model, which implies that the encoding mechanism of the DIS model might be more effective than that of the DOG model.

A new method for multiple spike train analysis based on information discrepancy

Simultaneous recording of multiple spike trains from population of neurons provides the possibility for understanding how neurons work together in response to various stimulations. But currently method is still lacking for researchers to perform multiple spike train data analysis and those existing techniques either allow people to analyze pair-wise neuronal activities only or are seriously subject to the selection of parameters. In this paper, a new measurement of information discrepancy, which is based on the comparisons of subsequence distributions, is applied to deal with a group of spike trains (n > 2) and analyze the synchronization pattern among the neurons, where the analytical result mostly depends on the experimental data and is affected little by subjective interference.

Effect of the Entorhinal Cortex on Ictal Discharges in Low-Mg2+-Induced Epileptic Hippocampal Slice Models

The hippocampus plays an important role in the genesis of mesial temporal lobe epilepsy, and the entorhinal cortex (EC) may affect the hippocampal network activity because of the heavy interconnection between them. However, the mechanism by which the EC affects the discharge patterns and the transmission mode of epileptiform discharges within the hippocampus needs further study. Here, multielectrode recording techniques were used to study the spatiotemporal characteristics of epileptiform discharges in adult mouse hippocampal slices and combined EC-hippocampal slices and determine whether and how the EC affects the hippocampal neuron discharge patterns. The results showed that low-Mg2+ artificial cerebrospinal fluid induced interictal discharges in hippocampal slices, whereas, in combined EC-hippocampal slices the discharge pattern was alternated between interictal and ictal discharges, and ictal discharges initiated in the EC and propagated to the hippocampus. The pharmacological effect of the antiepileptic drug valproate (VPA) was tested. VPA reversibly suppressed the frequency of interictal discharges but did not change the initiation site and propagation speed, and it completely blocked ictal discharges. Our results suggested that EC was necessary for the hippocampal ictal discharges, and ictal discharges were more sensitive than interictal discharges in response to VPA.

Gap junction permeability modulated by dopamine exerts effects on spatial and temporal correlation of retinal ganglion cells' firing activities

Synchronized activities among retinal ganglion cells (RGCs) via gap junctions can be increased by exogenous dopamine (DA). During DA application, single neurons’ firing activities become more synchronized with its adjacent neighbors. One intriguing question is how the enhanced spatial synchronization alters the temporal firing structure of single neurons. In the present study, firing activities of bullfrog’s dimming detectors in response to binary pseudo-random checker-board flickering were recorded via a multi-channel recording system. DA was applied in the retina to modulate synchronized activities between RGCs, and the effect of DA on firing activities of single neurons was examined. It was found that, during application of DA, synchronized activities between single neuron and its neighboring neurons was enhanced. At the meantime, the temporal structures of single neuron spike train changed significantly, and the temporal correlation in single neuron’s response decreased. The pharmacological study results indicated that the activation of D1 receptor might have effects on gap junction permeability between RGCs. Our results suggested that the dopaminergic pathway participated in the modulation of spatial and temporal correlation of RGCs’ firing activities, and may exert critical effects on visual information processing in the retina.